Technology Readiness and Acceptance Model (TRAM) - Lin et al. (2007)

Theory Publication Information

Citation Information

Why Was the Model Created?

Lin, Shih, and Sher developed the Technology Readiness and Acceptance Model to address a significant gap in technology adoption research. The Technology Acceptance Model had established that perceived usefulness and perceived ease of use predicted adoption intention, successfully explaining significant variance in acceptance behavior. However, TAM researchers had noted that individual differences in predisposition toward technology affected how users processed usefulness and ease of use perceptions. Some users appeared naturally optimistic about technology benefits and eager to learn new systems, while others remained skeptical, anxious, and resistant despite clear performance advantages. These individual differences seemed to influence whether users would adopt technology even when presented with identical benefits and usability information.

Parasuraman’s Technology Readiness Index offered a complementary perspective by measuring personality dimensions that shape technology adoption orientation: optimism (positive attitudes toward technology), innovativeness (propensity to adopt innovations), discomfort (psychological discomfort with technology), and insecurity (skepticism about technology capabilities). The TRI identified people high in optimism and innovativeness and low in discomfort and insecurity as more technology-ready. However, the TRI focused on trait-level predispositions without explicitly integrating with behavioral acceptance mechanisms like perceived usefulness and ease of use. Lin, Shih, and Sher recognized that technology readiness and technology acceptance were complementary but independent frameworks: readiness reflected personality traits shaping general technology orientation, while acceptance reflected situation-specific evaluations of particular technologies.

The authors developed TRAM to bridge these frameworks by hypothesizing that technology readiness dimensions would shape how individuals formed perceived usefulness and perceived ease of use judgments. Specifically, they proposed that users high in optimism and innovativeness would more readily perceive technologies as useful, while users high in discomfort and insecurity would perceive greater complexity and difficulty. By integrating readiness and acceptance, TRAM would provide more complete explanation of technology adoption, capturing both personality-driven predispositions and situation-specific technology evaluations. Testing occurred via Web-based surveys in March-April 2004 with 406 members of online investment discussion forums in Taiwan, examining adoption of online stock trading systems - a context where readiness personality dimensions might significantly shape perceptions of utility and usability.

What Does the Model Measure?

TRAM measures seven latent constructs using 7-point Likert scales (strongly disagree = 1 to strongly agree = 7). Measures originally in English were translated into Chinese and back-translated (Brislin, 1980) for the Taiwan-based Web survey. Cronbach’s alpha reliabilities were strong across all constructs (all ≥0.90):

• Optimism(10 items, α=0.95): From Parasuraman’s (2000) Technology Readiness Index (TRI); positive view of technology and its control, flexibility, and efficiency benefits.
• Innovativeness (7 items, α=0.95): From TRI; tendency to be a technology pioneer and thought leader.
• Discomfort (10 items, α=0.90): From TRI; perception of lack of control over technology and feeling overwhelmed.
• Insecurity (9 items, α=0.92): From TRI; distrust of technology and skepticism about its ability to work properly.
• Perceived Usefulness (6 items, α=0.95): Adapted from Davis (1989); belief that the e-service enhances performance.
• Perceived Ease of Use (6 items, α=0.96): Adapted from Davis (1989); belief that using the e-service is free of effort.
• Use Intention (2 items, α=0.92): Study-specific items regarding intent to use an online stock trading system on the next trade and in the next few months.

Confirmatory factor analysis in Amos 4 indicated adequate fit: GFI=0.90, CFI=0.96, TLI=0.95, RMSEA=0.07, χ²(126)=404.81, p<0.01. Average standardized factor loading was 0.80 with all loadings highly significant (p<0.01); discriminant validity was confirmed by confidence-interval tests that excluded 1.0 for each pairwise construct correlation (Anderson & Gerbing, 1988).

Core Concepts and Definitions

TRAM integrates technology readiness personality dimensions with technology acceptance mechanisms:

• Optimism: A personality dimension reflecting positive attitudes toward technology, belief that technology offers increased control, flexibility, and efficiency, and confidence that technology will provide benefits. Optimistic individuals perceive greater usefulness in technologies and are more receptive to adoption opportunities.
• Innovativeness: A personality dimension reflecting predisposition to adopt innovations, tendency to be among first users of new technologies, and willingness to experiment with novel applications. Innovativeness directly relates to perceiving new technologies as useful for exploring capabilities and achieving performance advantages.
• Discomfort: A personality dimension reflecting psychological discomfort when interacting with technology, concerns about losing control or understanding, and perception of technology as complex and difficult. Discomfort directly increases perception of ease of use barriers and reduces willingness to engage with technologies.
• Insecurity: A personality dimension reflecting skepticism about technology capabilities, doubts that technology will perform as promised, and concerns about security and privacy risks. Insecurity reduces perception of usefulness and increases fear that technology adoption will expose users to unacceptable risks.
• Perceived Usefulness (TAM): Individual beliefs that using a particular technology will improve job performance or achieve valued outcomes. Perceived usefulness represents situation-specific evaluation of technology benefits shaped by underlying optimism and innovativeness predispositions.
• Perceived Ease of Use (TAM): Individual beliefs about the degree of effort required to learn and operate a technology. Perceived ease of use represents situation-specific evaluation of technology complexity shaped by underlying discomfort and insecurity predispositions.
• Adoption Intention: The degree to which an individual intends to adopt and use technology, determined jointly by perceived usefulness and perceived ease of use, which in turn are shaped by technology readiness personality dimensions.

Preceding Models or Theories

TRAM explicitly builds on two prior frameworks:

• Technology Acceptance Model (Davis, 1989): Established perceived usefulness and perceived ease of use as primary predictors of adoption intention and behavior. TAM demonstrated that technology-specific beliefs about performance benefits and learning requirements predicted acceptance across diverse technologies. TRAM incorporates TAM’s core mechanisms while adding personality-level antecedents.
• Technology Readiness Index (Parasuraman, 2000): Identified four personality dimensions influencing general technology orientation: optimism, innovativeness, discomfort, and insecurity. The TRI demonstrated that individuals varied systematically in readiness to adopt new technologies based on these stable personality traits. TRAM operationalizes TRI dimensions as antecedents shaping TAM belief formation.
• Theory of Reasoned Action (Fishbein & Ajzen, 1975): Foundational framework establishing that behavioral intention determined by attitudes and subjective norms predicts actual behavior. Both TAM and TRAM incorporate TRA’s intention-behavior logic as fundamental mechanism.
• Diffusion of Innovations (Rogers, 2003): Established that consumer innovativeness and other diffusion attributes predict adoption of new products and services. Lin, Shih, and Sher (2007, p.645) explicitly cite Rogers (2003, 5th ed.) when grounding the role of prior experience and individual differences in technology adoption. TRAM draws on this tradition by incorporating innovativeness as a personality dimension shaping adoption.

Describe The Model

TRAM proposes that Technology Readiness (TR), treated as a higher-order construct with four reflective sub-dimensions (optimism, innovativeness, discomfort, insecurity), directly influences how individuals form technology-specific beliefs about usefulness and ease of use, which in turn determine adoption intention. The paper (Figure 1, p.646) hypothesizes paths from the aggregate TR construct to perceived usefulness (H5) and to perceived ease of use (H6), rather than separate paths from each sub-dimension. The sub-dimensions serve as indicators of TR: optimism and innovativeness are drivers (more TR); discomfort and insecurity are inhibitors (less TR). Perceived usefulness and perceived ease of use then predict use intention per the standard TAM mechanisms.

TRAM Hypotheses (as tested in Lin et al., 2007)

• H1: TR → Use Intention (direct):Consumers’ technology readiness propensities are positively correlated with their intentions to use a specific e-service. This direct path is replicated from prior TR research.
• H2: Perceived Usefulness → Use Intention: Standard TAM path; PU positively correlates with intention to use the e-service.
• H3: Perceived Ease of Use → Use Intention: Standard TAM path; PEOU positively correlates with intention to use the e-service.
• H4: Perceived Ease of Use → Perceived Usefulness: Standard TAM path; PEOU positively influences PU.
• H5: TR → Perceived Usefulness:The paper’s focal hypothesis. Technology readiness (aggregate construct) positively correlates with perceptions of usefulness about a specific e-service.
• H6: TR → Perceived Ease of Use:The paper’s focal hypothesis. Technology readiness (aggregate construct) positively correlates with perceptions of ease of use about a specific e-service.
• H7: Full mediation: PU and PEOU together completely mediate the TR-to-intention relationship, such that the direct H1 path becomes non-significant once H5 and H6 are estimated. Tested via Baron and Kenny (1986) mediation procedure and Sobel tests.

Key Results (Trimmed Model, Table 2 p.650)

The trimmed integrated model (with the TR-to-UI path constrained to zero) was preferred over the full model on parsimony grounds (Δχ²=1.97, df=1, p=0.16). Standardized path coefficients were all significant at p<0.01:

• TR → PEOU: 0.74 (strongest effect - H6 supported)
• PU → UI: 0.59 (standard TAM path - H2 supported)
• TR → PU: 0.52 (H5 supported)
• PEOU → PU: 0.30 (standard TAM path - H4 supported)
• PEOU → UI: 0.23 (standard TAM path - H3 supported)

Standardized total effects on Use Intention(p.651): TR = 0.60, PU = 0.59, PEOU = 0.40. The paper’s key insight: TR’s effect on intention operates primarily through PEOU, not through PU, because TR’s direct impact on PEOU (0.74) is much stronger than its impact on PU (0.52). The overall psychological process is consistent with a TR → PEOU → PU → UI chain of causality.

Main Strengths

• Bridges personality and situation-specific factors: TRAM elegantly integrates stable personality dimensions with situation-specific technology beliefs, showing how dispositional traits shape evaluations of particular technologies.
• Explains individual differences in technology perception: Model accounts for why identical technologies receive different acceptance evaluations from different users, rooted in underlying personality differences.
• Parsimoniousness with comprehensive coverage: TRAM adds only four personality dimensions to TAM rather than creating entirely new framework, maintaining relative simplicity while substantially expanding explanatory scope.
• Empirical validation in consumer context: Testing with 406 Taiwanese investors examining online stock trading system adoption demonstrates applicability to voluntary consumer adoption contexts, not just organizational mandatory settings.
• Structural equation modeling with fit indices: Study reported appropriate SEM model fit statistics, demonstrating that proposed relationships among constructs fit data better than alternative specifications.
• Differential effects by readiness dimension: Results demonstrated that readiness dimensions affected acceptance through theorized pathways, with optimism influencing both usefulness and ease of use, innovativeness influencing usefulness, discomfort influencing ease of use, and insecurity influencing usefulness.
• Practical segmentation implications: Model suggests that market segmentation by technology readiness personality could predict which customers would adopt technologies and which require different approaches.

Main Weaknesses

• Limited sample diversity: Single study with 406 respondents in Taiwan recruited from online investment forums; limits generalization to organizational information systems, other technologies, or non-Asian markets.
• Cross-cultural replicability unclear: Technology readiness dimensions may operate differently in different cultural contexts with varying attitudes toward technology and different gender role norms.
• Personality stability assumptions untested: Model assumes technology readiness dimensions remain stable across contexts and time, but does not test whether readiness changes through experience or organizational socialization.
• No actual behavior measurement: Study measured adoption intention but not actual adoption or usage behavior, limiting ability to confirm that intention-behavior relationships extend to TRAM context.
• Causality direction not definitively established: While theoretical model proposes personality shapes belief formation, cross-sectional design cannot rule out reverse causality where technology experiences shape personality perceptions.
• Mediation mechanisms not fully explored: Model specifies that readiness dimensions affect adoption through usefulness and ease of use perceptions but does not deeply examine what psychological mechanisms produce these relationships.
• Missing moderators: Model does not consider organizational context, implementation support, peer influence, or training effects that might moderate readiness-belief relationships.
• Technology specificity unexamined: Single e-service context studied. Readiness dimensions might affect different technology types differently, with discomfort particularly important for complex systems and innovativeness more critical for novel technologies.

Key Contributions

• Integration of personality and technology adoption: Demonstrated that personality dimensions predict technology adoption through their influence on technology-specific beliefs, integrating trait psychology with technology adoption research.
• Operationalization of TRI in adoption context: Showed how Technology Readiness Index dimensions function as antecedents in established adoption models, operationalizing consumer personality research within technology acceptance frameworks.
• Full-mediation evidence: Provided empirical evidence (via Baron and Kenny 1986 tests plus Sobel tests) that perceived usefulness and perceived ease of use together fully mediate the TR-to-intention relationship, supporting H7. Direct TR-to-intention effects become non-significant when TAM beliefs are controlled.
• Consumer e-service adoption explanation: Applied technology acceptance to the voluntary consumer adoption context of online stock trading, extending adoption research beyond organizational mandatory settings.
• Practical segmentation approach: Demonstrated that technology readiness personality could segment customer populations likely to resist or readily adopt e-services, enabling targeted marketing strategies.
• Bridge between marketing and IS disciplines: Connected consumer behavior and personality research from marketing with information systems technology adoption research, facilitating cross-disciplinary dialogue.
• Expanded TAM explanatory scope: Maintained TAM core mechanisms while accounting for individual differences through personality antecedents, improving model completeness without abandoning proven framework.

Internal Validity

TRAM study employed appropriate methodology to establish relationships among constructs:

• Measurement of established constructs: Perceived usefulness, perceived ease of use, and adoption intention measured using validated TAM scales established in prior research.
• Technology readiness operationalization: Technology Readiness Index dimensions measured using established Parasuraman TRI items validated in prior consumer research.
• Adequate sample size: N=406 online investor respondents (64% male, 57% aged 21-30) provided sufficient power to detect relationships and test structural equation model fit; 85% had prior stock trading experience and 80% had online stock trading experience.
• Structural equation modeling in Amos 4:The integrated model was estimated in Amos 4 (Arbuckle & Wothke, 1999). Full and trimmed (nested) models were estimated: the trimmed model constrained the TR-to-UI path to zero. The chi- square difference was non-significant (Δχ²=1.97, df=1, p=0.16), so the trimmed model was preferred.
• Real e-service context: Study examined an actual online stock trading system rather than hypothetical or artificial technologies, ensuring adoption context realism.
• Mediation testing:Baron and Kenny (1986) three-equation mediation procedure plus Sobel (1982) tests (z=5.23 for TR→PU→UI; z=2.73 for TR→PEOU→UI; both p<0.01) supported H7 (full mediation via PU and PEOU).
• Effect direction consistency: Results confirmed the a priori hypothesized positive path from aggregate TR to both perceived usefulness (H5) and perceived ease of use (H6), with optimism and innovativeness as positive reflective indicators and discomfort and insecurity as negative reflective indicators of TR.

External Validity

External validity considerations require careful interpretation of generalizability:

• Single technology domain limitation: Study examined online stock trading systems specifically. Readiness dimensions may affect different technology types differently, with results requiring verification across diverse technologies.
• Geographic and cultural context: Taiwan e-service users may show different readiness-acceptance relationships than users in Western contexts with different cultural attitudes toward technology and different gender role norms.
• Consumer versus organizational adoption: TRAM addresses voluntary consumer technology adoption. Applicability to mandatory organizational adoption contexts requires testing.
• Technology novelty considerations: Study examined relatively established e-services in 2007. Readiness dimensions might affect adoption of radically novel technologies differently than established services.
• Internet experience skew: 80% of respondents had online stock trading experience and 85% had prior stock trading experience, creating a sample skewed toward technology-experienced users. Generalization to low-technology-experience populations is uncertain.
• Demographic skew: 64% male, 57% aged 21-30, 18% aged 31-40, 17% under 21; drawn from online investment discussion forums. Results may not generalize to older adults, women, or less technology-engaged demographic groups.
• Cross-sectional design limitations: Single-point measurement limits ability to assess whether personality-belief-adoption relationships sustain over time or whether experience changes readiness and adoption dynamics.
• Personality stability across contexts: Assumptions about technology readiness stability untested. Individuals might show different readiness dimensions for different technologies or in different organizational settings.

Relevance to Technology Adoption

TRAM directly addresses technology adoption barriers by illuminating how personality dimensions create differential technology adoption risks. The model demonstrates that technology adoption barriers manifest through personality-shaped belief formation: optimistic individuals perceive fewer barriers regardless of objective technology complexity, while anxious and skeptical individuals perceive greater barriers even when technologies are genuinely useful and easy. This suggests adoption barriers operate partly through personality lenses rather than purely through technology characteristics. Organizations implementing technology must address personality-driven barriers, recognizing that identical implementation approaches will differentially affect users with different readiness profiles. Users high in discomfort and insecurity require greater assurance about ease of use and security, while skeptical users need credible performance evidence. The model suggests that targeted communication, training, and implementation support should align with readiness personality profiles.

Personality-Driven Adoption Barriers

• Discomfort-driven complexity perception: Users high in discomfort perceive greater complexity even in user-friendly systems, creating significant adoption barriers for anxious populations.
• Insecurity-driven performance doubts: Skeptical users doubt performance benefits even when objective evidence supports usefulness claims, requiring extraordinary persuasion to overcome adoption resistance.
• Low innovativeness limitations: Non-innovative users may resist adoption of genuinely useful technologies if positioned as cutting-edge or novel, preferring familiar approaches.
• Pessimism-driven negative evaluation: Users low in optimism interpret ambiguous technology characteristics negatively, focusing on potential drawbacks rather than benefits.
• Personality-media fit mismatch: Technologies requiring independent learning or self-driven adoption face barriers with discomfort-prone users who need direct support and reassurance.

Leadership Actions the Model Prescribes

• Segment users by technology readiness profile: Identify which users are technology-ready (high optimism, innovativeness; low discomfort, insecurity) and which require additional support, enabling tailored implementation approaches.
• Emphasize ease of use with discomfort-prone users: For anxious populations, prioritize usability design, comprehensive training, readily available support, and frequent reassurance that systems are manageable.
• Provide credible performance evidence to skeptical users: Users high in insecurity require objective performance data, pilot programs demonstrating benefits, and peer validation rather than vendor promises.
• Frame adoption for different readiness profiles: Position technology as innovative and capability-expanding to innovators, while emphasizing proven benefits and ease of integration for conservative users.
• Dedicate resources to lower-readiness populations: Users low in optimism, innovativeness, and high in discomfort and insecurity require disproportionate implementation support, training, and encouragement compared to technology-ready populations.
• Build confidence through early wins: Enable lower-readiness users to achieve quick success with simple technology features before attempting complex functionality, building confidence that technology is manageable.
• Use peer champions strategically: High-readiness users can model adoption and serve as champions, providing confidence to anxious users that adoption is achievable.
• Communicate security and stability to insecure users: Users skeptical about technology performance require assurance about data security, system reliability, and vendor stability rather than performance potential.

Following Models or Theories

TRAM contributed to several subsequent research directions:

• Personality-extended adoption models:Researchers incorporated additional personality dimensions (technology anxiety, computer self-efficacy, personal innovativeness) into technology acceptance frameworks following TRAM’s integration approach.
• Technology readiness in organizational contexts:Subsequent studies examined whether Parasuraman’s TRI predicts adoption of enterprise systems, cloud services, and IT-enabled organizational changes.
• Consumer e-service adoption expansion: Research extended TRAM logic to mobile banking adoption, online shopping commitment, digital payment systems, and social media adoption in consumer contexts.
• Readiness × technology fit research: Studies investigated whether personality readiness dimensions interact with technology characteristics, with discomfort creating particular barriers for complex systems while innovativeness drives adoption of novel technologies.
• Segmentation and targeting approaches: Marketing and IT leaders adopted readiness-based segmentation to tailor adoption strategies and support resources to user populations with different personality profiles.
• Cross-cultural readiness studies: Researchers examined whether technology readiness dimensions operate consistently across cultures or whether cultural values moderate personality effects on adoption.
• Longitudinal personality-adoption research: Studies investigated whether personality dimensions predict sustained technology use beyond initial adoption and whether technology experience modifies personality readiness.
• Emotional and affective adoption models:Research integrated emotional responses and technology anxiety alongside TRAM’s personality dimensions to explain adoption resistance.

References

1. Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320. https://doi.org/10.1177/109467050024001
2. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
3. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
4. Lin, C.-H., Shih, H.-Y., & Sher, P. J. (2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing, 24(7), 641-657. https://doi.org/10.1002/mar.20177

Further Reading

1. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
2. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
3. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
4. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
5. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688
6. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
7. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143.
8. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model and research extensions. MIS Quarterly, 29(4), 573-596. https://doi.org/10.2307/25148690

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